1. Field of Invention
The present invention describes a monotonic optimization method for achieving the maximum weighted sum-rate in multicell downlink MISO systems, which is a technique of wireless communications.
2. Description of Related Arts
Coordinated multicell downlink transmission is a promising technique for suppressing co-channel interference and improving the system performance. Among the different kinds of design criteria, weighted sum-rate maximization is an active research topic, which has attracted significant attention from both academics and industries. However, achieving the global optimum is very difficult as the capacity region remains unknown. A more pragmatic method that adopts single-user detection by simply treating interference as noise has stimulated some efficient algorithms.
In the conventional techniques, the reference L. Venturino, N. Prasad, and X. Wang, “Coordinated linear beamforming in downlink multi-cell wireless networks,” IEEE Transactions on Wireless Communications, vol. 9, no. 4, pp. 1451-1461, April 2010, presents an iterative coordinated beamforming algorithm to solve the Karush-Kuhn-Tucker conditions of the weighted sum-rate maximization problem.
The reference Q. J. Shi, M. Razaviyayn, Z. Q. Luo, and C. He, “An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331-4340, September 2011, proposes a weighted minimum mean square error method which iteratively update the transmitter, the receiver and the weight matrix.
However, the achievable rate region under single-user detection remains non-convex. For MISO systems, the reference R. Zhang and S. G. Cui, “Cooperative interference management with MISO beamforming,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5454-5462, October 2010, proves that beamforming techniques can achieve the Pareto-optimal points and describes a method to characterize the Pareto-boundary of the achievable rate region. Although there are already some techniques obtaining Pareto-optimal solutions, it is still a big challenge to search for the global optimality.
The reference L. Liu, R. Zhang, and K. Chua, “Achieving global optimality for weighted sum-rate maximization in the K-user Gaussian interference channel with multiple antennas,” IEEE Transactions on Wireless Communications, vol. 11, no. 5, pp. 1933-1945, 2012, views the weighted sum-rate maximization as a monotonic optimization problem and adopts the outer polyblock approximation algorithm combined with the rate profile technique to approaching the global optimal solution. To improve the efficiency of the monotonic optimization, another reference E. Bjornson, G. Zheng, M. Bengtsson, and B. Ottersten, “Robust monotonic optimization framework for multicell MISO systems,” IEEE Transactions on Signal Processing, vol. 60, no. 5, pp. 2508-2523, 2012, provides a branch-reduce-and-bound based method. Although it converges faster than the outer polyblock approximation algorithm, it still needs too many iterations as the number of users increase.